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New approaches to Q-matrix validation and estimation for cognitive diagnosis models

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TitleInfo
Title
New approaches to Q-matrix validation and estimation for cognitive diagnosis models
Name (type = personal)
NamePart (type = family)
Oluwalana
NamePart (type = given)
Olasumbo O.
NamePart (type = date)
1971
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Olasumbo O. Oluwalana
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Chiu
NamePart (type = given)
Chia-Yi
DisplayForm
Chia-Yi Chiu
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-05
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
A primary purpose of cognitive diagnosis models (CDMs) is to classify examinees based on their attribute patterns. The Q-matrix (Tatsuoka, 1985), a common component of all CDMs, specifies the relationship between the set of required dichotomous attributes and the test items. Since a Q-matrix is often developed by content-knowledge experts and can be influenced by their judgment (de la Torre & Chiu, 2016), this can lead to misspecifications in the Q-matrix that can have unintended consequences on examinees’ classifications. Incorrect classification of examinees can have tremendous impact since some assessments are high-stake and are used to make important decisions about students, such as selection and placement. Previous research based on the Trends in International Math and Science Study (TIMSS) has predominantly focused on comparing the performances of participating countries using their average scores.
This study focused on fitting data from the TIMSS with a CDM to obtain estimated attribute profiles that will provide information about skill proficiency of students in the participating countries. However, since the test is not specifically designed for use with a CDM, a provisional Q-matrix was developed with input from content experts. As a preliminary analysis, the TIMSS data was first fitted with the generalized deterministic inputs, noisy, “and” gate (G-DINA) model to obtain examinees’ estimated attribute profiles. An evaluation of the estimated attribute profiles however indicated that there are inconsistencies in classification, which may be due to misspecification in the provisional Q-matrix. To ensure that the provisional Q-matrix is appropriately developed, this dissertation proposes one Q-matrix validation method that can be used to correct possible misspecifications in a Q-matrix, and one Q-matrix estimation method for estimating a Q-matrix from scratch.
The proposed methods both integrate the Q-matrix validation procedure (Chiu, 2013) that is based on a nonparametric classification method. The first method, the integrated Q-matrix validation (IQV) technique, uses a joint maximum likelihood estimation (JMLE) procedure for diagnostic classification models (Chiu, Köhn, Zheng, and Henson, 2016) to determine examinees’ attribute profiles that are then integrated into the algorithm of Chiu’s Q-matrix validation method to validate the Q-matrix. In the second method, the two-step Q-matrix estimation (TSQE) method, factor analysis is first applied to the correlation matrix to obtain a provisional Q-matrix. The provisional Q-matrix is then incorporated into the algorithm of Chiu’s Q-matrix validation method, to obtain the true Q-matrix.
The viability of both methods was investigated using simulation studies with various conditions. The TIMSS data was re-analyzed with the G-DINA model using modified Q-matrices obtained from analysis with the proposed methods. An evaluation of the updated estimated attribute profiles indicated that some of the inconsistencies in classification that were previously identified have been resolved.
Subject (authority = local)
Topic
Q-matrix
Subject (authority = RUETD)
Topic
Education
Subject (authority = LCSH)
Topic
Educational tests and measurements
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_9685
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application/pdf
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text/xml
Extent
1 online resource (x, 77 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-666a-s889
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Oluwalana
GivenName
Olasumbo
MiddleName
O.
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-04-04 21:43:06
AssociatedEntity
Name
OLASUMBO OLUWALANA
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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License
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-05-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2021-05-30
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 30th, 2021.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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